Enterprises are increasing their use of field-programmable gate array (FPGA) based computing systems (i.e., computer appliances) to meet requirements of real-time processing of large and bulk data volumes. An FPGA is defined as a logic device (i.e., logic chip) that includes static random access memory (SRAM) and an array of regularly tiled and interconnected logic elements, which are programmable in the field through different configurations. The configurations may be reprogrammed multiple times, thereby allowing massive instruction-level parallelism. The logic elements in an FPGA may include gates, lookup table random access memory (RAM), and flip-flops. An FPGA may also include programmable interconnects and programmable switches between the logic elements. FPGA based appliances (e.g., the Netezza® database appliance offered by International Business Machines Corporation located in Armonk, N.Y.) are used to develop massive data processing applications involving data warehouses and data mining. FPGA based appliances are used for designing scalable, high-performance, massively parallel analytic platforms designed to manage data volumes on a petabyte scale. Data that is relevant today for a business purpose and direction becomes obsolete over time due to rapid changes in the business environment and the way business is conducted, thereby rendering irrelevant data on the analytical models based on datasets using predefined or static rules. Furthermore, analytics logic using diverse datasets is severely constrained by manual, error-prone identification of relationships across datasets and data elements. The most common single input for building an analytics model is human knowledge, which may not generate the needed results in analytics because of the requirement of data sizes and the fitment to business situation requirements which are not rationalized.